Balancing Act: Precision Medicine and National Security

ABSTRACT Developments in genetics, pharmacology, biomarker identification, imaging, and interventional biotechnology are enabling medicine to become increasingly more precise in “personalized” approaches to assessing and treating individual patients. Here we describe current scientific and technological developments in precision medicine and elucidate the dual-use risks of employing these tools and capabilities to exert disruptive influence upon human health, economics, social structure, military capabilities, and global dimensions of power. We advocate continued enterprise toward more completely addressing nuances in the ethical systems and approaches that can—and should—be implemented (and communicated) to more effectively inform policy to guide and govern the biosecurity and use of current and emerging bioscience and technology on the rapidly shifting global stage.

Human Microbiome in Children, at the Crossroad of Social Determinants of Health and Personalized Medicine

The evolving field of microbiome research offers an excellent opportunity for biomarker identification, understanding drug metabolization disparities, and improving personalized medicine. However, the complexities of host–microbe ecological interactions hinder clinical transferability. Among other factors, the microbiome is deeply influenced by age and social determinants of health, including environmental factors such as diet and lifestyle conditions. In this article, the bidirectionality of social and host–microorganism interactions in health will be discussed. While the field of microbiome-related personalized medicine evolves, it is clear that social determinants of health should be mitigated. Furthermore, microbiome research exemplifies the need for specific pediatric investigation plans to improve children’s health.

TiMEG: an integrative statistical method for partially missing multi-omics data

Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case–control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omic analysis or common imputation-based methods. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.

Evaluation of Anti-Inflammatory Effects of Celery Leaf and Stem Extracts in LPS-Induced RAW 264.7 Cells Using Nitric Oxide Assay and LC-MS Based Metabolomics

The present work demonstrated and compared the anti-inflammatory effects of celery leaf (CLE) and stem (CSE) extracts. LC-MS-based metabolomics were an effective approach to achieve the biomarker identification and pathway elucidation associated with the reduction in inflammatory responses. The celery extracts suppressed LPS-induced NO production in RAW 264.7 cells, and CLE was five times more effective than CSE. Distinct differences were revealed between the control and celery-treated samples among the 24 characteristic metabolites that were identified. In celery-treated LPS cells, reversals of intracellular (citrulline, proline, creatine) and extracellular (citrulline, lysine) metabolites revealed that the therapeutic outcomes were closely linked to arginine metabolism. Reversals of metabolites when treated with CLE (aspartate, proline) indicated targeted effects on the TCA and urea cycles, while, in the case of CSE (histidine, glucose), the glycolysis and the pentose phosphate pathways were implicated. Subsequently, apigenin and bergapten in CLE were identified as potential biomarkers mediating the anti-inflammatory response.

Infer disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data

Unveiling disease-associated microbial biomarkers (e.g., key species, genes, and pathways) is an efficient strategy for the diagnosis and therapy of diseases. However, the heterogeneity and large size of microbial data bring tremendous challenges for fundamental characteristics discovery. We present IDAM, a novel method for disease-associated biomarker identification from metagenomic and metatranscriptomic data, without requiring prior metadata. It integrates gene context conservation and regulatory mechanism through a mathematical model for maximizing the number of connected components between local-low rank submatrices of a gene expression matrix and known uber-operon structures. We applied IDAM to 813 inflammatory bowel disease-associated datasets and showed IDAM outperformed existing methods in microbial biomarker identification. In addition, the identified biomarkers successfully distinguished disease subtypes and showcased their power in discovering novel disease subtypes/states. IDAM is freely available at https://github.com/OSU-BMBL/IDAM.